@article{1461650,
      note = {5.4.3 Procedure of the Real-Time System Identification  with Self-Calibratable Model Classes},
      author = {Huang, Ke, and Yuen, Ka-Veng,},
      url = {http://library.usi.edu/record/1461650},
      title = {Bayesian real-time system identification : from  centralized to distributed approach /},
      publisher = {Springer,},
      abstract = {This book introduces some recent developments in Bayesian  real-time system identification. It contains two different  perspectives on data processing for system identification,  namely centralized and distributed. A centralized Bayesian  identification framework is presented to address  challenging problems of real-time parameter estimation,  which covers outlier detection, system, and noise  parameters tracking. Besides, real-time Bayesian model  class selection is introduced to tackle model  misspecification problem. On the other hand, a distributed  Bayesian identification framework is presented to handle  asynchronous data and multiple outlier corrupted data. This  book provides sufficient background to follow Bayesian  methods for solving real-time system identification  problems in civil and other engineering disciplines. The  illustrative examples allow the readers to quickly  understand the algorithms and associated applications. This  book is intended for graduate students and researchers in  civil and mechanical engineering. Practitioners can also  find useful reference guide for solving engineering  problems.},
      doi = {https://doi.org/10.1007/978-981-99-0593-5},
      recid = {1461650},
      pages = {1 online resource (xii, 276 pages) :},
      address = {Singapore :},
      year = {2023},
}